Using GPS-Enabled Smartphones and Geofence to Capture Traffic Data on Urban Freeways and Arterials
Bibliographic record
Abstract
Global Positioning Systems (GPS) have emerged as the leading technology to provide location information to various location-based services. With an increasing smartphone penetration rate, as well as expanding spatial and network coverage, the idea of combining GPS positioning functions with smartphone platforms to perform GPS-enabled smartphone-based traffic management and data monitoring is promising. This study presents a field experiment conducted along Whitemud Drive freeway (in Edmonton, Alberta, Canada) and an urban arterial, using a combination of GPS-enabled smartphones and geofences. Relative positioning and timestamp errors at geofence locations were estimated. Traffic state information, such as link travel speed and link travel time, was collected using a variety of Android and iOS smartphones and evaluated against ground truth data. The performance of the experimental setting is discussed in this study, and the results indicate that geofences deployed on freeways perform better than when deployed on arterials, and deployment on arterials also requires more caution. A combination of Smartphone GPS and geofence may be capable of capturing a greater range of speed than the loop detector, and the estimated link travel speed may also be more accurate than that measured by the loop detector. When estimating traffic state parameters, Android smartphones perform better than iOS smartphones in most cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".